Since the first loans to buy grain were issued in 2000 BC, lenders have understood that knowing as much about the applicant’s business as possible is the best way to understand risk and predict the likelihood of default. Fast forward a few thousand years and the amount of data available has grown by infinite proportions, but just because the data is available doesn’t guarantee that it’s either easily accessible or in a useable form. Sticking with the car analogy, your old-school lending processes have serious problems when it comes to data integration and real-time risk analytics. Think of manual decisioning like your 80’s limo. Need to gather information? Data access and orchestration means making regular pitstops at data sources to manually collect the information you need, and often asking your passengers to do some of the work for you. For them it’s like hiring a taxi then being expected to take on a significant share of the driving. If this happened in any other industry, a one-star Yelp review would be considered generous. The issue is that when you’re driving an 80s vehicle, ‘connectedness’ means a car phone that cuts out at the slightest hint of signal interference. Gathering data isn’t a simple or fast task and keeping applicants up-to-date requires extensive communication. When your team enters the decision processing tunnel to assess the loan you may as well have been sucked into a black hole, leaving applicants asking:
To deliver real-time decisions that support world-class SME lending experiences, you need to upgrade to a solution that makes it quick and easy to pull in the right data exactly when it’s needed. This means no longer relying on personal credit scores to decision business loans, but instead looking to business financial data, such as accounting documents and tax returns, to get a deeper understanding of business health and decisioning loans based on a prediction of future business success. When you have this volume of data and the technology to access, orchestrate and process it, you can implement risk decisioning tools that can accurately take an application from data to decision in under a second. We’ll cover this more in the “choosing the right technology” section that follows.
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